Jose Dolz

According to our database1, Jose Dolz authored at least 75 papers between 2014 and 2022.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty.
IEEE Trans. Medical Imaging, 2022

Weakly supervised segmentation with cross-modality equivariant constraints.
Medical Image Anal., 2022

Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images.
CoRR, 2022

Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation.
CoRR, 2022

On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation.
CoRR, 2022

Constrained unsupervised anomaly segmentation.
CoRR, 2022

2021
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images.
IEEE Trans. Medical Imaging, 2021

Constrained Domain Adaptation for Image Segmentation.
IEEE Trans. Medical Imaging, 2021

Multi-Scale Self-Guided Attention for Medical Image Segmentation.
IEEE J. Biomed. Health Informatics, 2021

Self-Learning for Weakly Supervised Gleason Grading of Local Patterns.
IEEE J. Biomed. Health Informatics, 2021

Boundary loss for highly unbalanced segmentation.
Medical Image Anal., 2021

Knowledge distillation methods for efficient unsupervised adaptation across multiple domains.
Image Vis. Comput., 2021

Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation.
CoRR, 2021

The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration.
CoRR, 2021

Mixed-supervised segmentation: Confidence maximization helps knowledge distillation.
CoRR, 2021

Looking at the whole picture: constrained unsupervised anomaly segmentation.
CoRR, 2021

Source-Free Domain Adaptation for Image Segmentation.
CoRR, 2021

Mutual-Information Based Few-Shot Classification.
CoRR, 2021

Transductive Few-Shot Learning: Clustering is All You Need?
CoRR, 2021

The hidden label-marginal biases of segmentation losses.
CoRR, 2021

Incremental Multi-Target Domain Adaptation for Object Detection with Efficient Domain Transfer.
CoRR, 2021

Bladder segmentation based on deep learning approaches: current limitations and lessons.
CoRR, 2021

MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons.
Comput. Biol. Medicine, 2021

Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

On the Texture Bias for Few-Shot CNN Segmentation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021

Beyond pixel-wise supervision for segmentation: A few global shape descriptors might be surprisingly good!
Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Orthogonal Ensemble Networks for Biomedical Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

Teach Me to Segment with Mixed Supervision: Confident Students Become Masters.
Proceedings of the Information Processing in Medical Imaging, 2021

A Self-Training Framework for Glaucoma Grading In OCT B-Scans.
Proceedings of the 29th European Signal Processing Conference, 2021

Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Discretely-constrained deep network for weakly supervised segmentation.
Neural Networks, 2020

Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy.
CoRR, 2020

The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models.
CoRR, 2020

Transductive Information Maximization For Few-Shot Learning.
CoRR, 2020

Manifold-driven Attention Maps for Weakly Supervised Segmentation.
CoRR, 2020

Semi-supervised few-shot learning for medical image segmentation.
CoRR, 2020

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation.
Comput. Medical Imaging Graph., 2020

Information Maximization for Few-Shot Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Direct Distribution Matching for Adapting Segmentation Networks.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Bounding boxes for weakly supervised segmentation: Global constraints get close to full supervision.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Source-Relaxed Domain Adaptation for Image Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Laplacian Regularized Few-Shot Learning.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge.
IEEE Trans. Medical Imaging, 2019

HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation.
IEEE Trans. Medical Imaging, 2019

Constrained-CNN losses for weakly supervised segmentation.
Medical Image Anal., 2019

Privacy-Net: An Adversarial Approach For Identity-obfuscated Segmentation.
CoRR, 2019

Deep weakly-supervised learning methods for classification and localization in histology images: a survey.
CoRR, 2019

Revisiting CycleGAN for semi-supervised segmentation.
CoRR, 2019

Weakly Supervised Object Localization using Min-Max Entropy: an Interpretable Framework.
CoRR, 2019

Multi-scale guided attention for medical image segmentation.
CoRR, 2019

Log-barrier constrained CNNs.
CoRR, 2019

Curriculum Semi-supervised Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Constrained Domain Adaptation for Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

2018
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.
NeuroImage, 2018

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images.
NeuroImage, 2018

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning.
CoRR, 2018

Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks.
CoRR, 2018

Constrained-CNN losses forweakly supervised segmentation.
CoRR, 2018

IVD-Net: Intervertebral Disc Localization and Segmentation in MRI with a Multi-modal UNet.
Proceedings of the Computational Methods and Clinical Applications for Spine Imaging, 2018

Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Isointense infant brain segmentation with a hyper-dense connected convolutional neural network.
Proceedings of the 15th IEEE International Symposium on Biomedical Imaging, 2018

An Attention Model for Group-Level Emotion Recognition.
Proceedings of the 2018 on International Conference on Multimodal Interaction, 2018

2017
HyperDense-Net: A hyper-densely connected CNN for multi-modal image semantic segmentation.
CoRR, 2017

A 3D fully convolutional neural network and a random walker to segment the esophagus in CT.
CoRR, 2017

A deep learning classification scheme based on augmented-enhanced features to segment organs at risk on the optic region in brain cancer patients.
CoRR, 2017

Unbiased Shape Compactness for Segmentation.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017

DOPE: Distributed Optimization for Pairwise Energies.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016
User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy.
J. Digit. Imaging, 2016

Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study.
Comput. Medical Imaging Graph., 2016

Supervised machine learning-based classification scheme to segment the brainstem on MRI in multicenter brain tumor treatment context.
Int. J. Comput. Assist. Radiol. Surg., 2016

2015
A fast and fully automated approach to segment optic nerves on MRI and its application to radiosurgery.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015

2014
Combining watershed and graph cuts methods to segment organs at risk in radiotherapy.
Proceedings of the Medical Imaging 2014: Image Processing, 2014

Interactive approach to segment organs at risk in radiotherapy treatment planning.
Proceedings of the Medical Imaging 2014: Image Processing, 2014


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